Abstract

With the rapid growth in data, it turns very important to visualise them to get the inferences. Web, being the most commonly used platform, this talk is on how to work with large data in browsers. We’ll talk about the common experiences we encounter on attempting to do so. Firstly we’ll try to manage the data points effectively using data aggregation or over-plotting reduction.We will see what are the problems those are yet to be addressed even after the above operations. How to render the entire large data without a script error along with smooth interaction? Is SVG an option for it or does canvas stands better at it? What are the limit for canvas, and can we strech it? The talk explains what is batch rendering and how it helps in enhanching the performance with a live illustration.But using canvas comes at a cost of interaction support. We’ll see how to mock browser events algorithimically. And thereby, introducing kd-tree, explaining the benifits and discussing its performance limitations. It continues on how improving the tree-building implementaion provided that extra-edge. Discussing the boost recieved and trade-offs considered while designing the modified kd-tree version. Lastly, the 9-grid pre-rendering algortihim will be discussed to attain an effective zooming and panning user interface with live illustrations.

Requirements

Basic understanding of Javascript.

Speaker bio

I am a Javascript developer, currently working at FusionCharts. I have deep love for writing JS codes, especially in VanillaJS. I am always keen to learn and improve myself. Apart from them I love robotics and trying out small stuffs using my Arduino.